Cost-Effective Aerial Inventory of Spruce Seedlings Using Consumer Drones and Deep Learning Techniques with Two-Stage UAV Flight Patterns

Abstract

Traditional methods of counting seedling inventory are expensive, time-consuming, and lacking in spatial resolution. Although previous studies have explored the use of drones for seedling inventory, a cost-effective and accurate solution that can detect and identify missing seedlings at a high spatial resolution using consumer drones with traditional RGB cameras is needed. This study aims to address this research gap by developing such a solution using deep learning techniques. A two-stage drone flight pattern was employed to collect high-resolution data (2.22 mm). Firstly, a flight was conducted at a 120 m altitude to generate an obstacle map. This map was then used to conduct a second flight at a 5 m altitude, avoiding collision with larger trees. Convolutional neural networks were used to detect planted spruce seedlings with high accuracy (mean average precision of 84% and detection accuracy of 97.86%). Kernel density estimation was utilized to identify areas with missing seedlings. This study demonstrates that consumer drones and deep learning techniques can provide a cost-effective and accurate solution for taking aerial inventories of spruce seedlings. The two-stage flight pattern used in this study allowed for safe and efficient data collection, while the use of convolutional neural networks and kernel density estimation facilitated the accurate detection of planted seedlings and identification of areas with missing seedlings.